Analysis of Grap 1: Laws on Domestic Violence by Country Income Group
The graph shows that most countries, regardless of income group, fall in the “0.75” measure for Laws on Domestic Violence. That means that their laws and practices are unsuficcient to garantee the well-being of women. It is evident that even though there are few countries in which the number reaches the value 1 (laws and practices are completely discriminatory against women), the realization that most countries in the global still have unsuficcient laws that protect women’s rights is incredibly worrying. We can also observe that there are more countries with better laws and practices for women’s rights among high income countries (that is, countries in which the indicator equals 0.25). This also holds true in a lesser extent for upper middle income countries. For both lower middle income and low income, however, there seems to be more countries in the 0.5 and 0.75 levels then in the 0.25. This unvels a pattern that establishes that higher income coutries, despite also having large number of countries that have a indicator of law on domestic violence equal to 0.75, have also higher number of countries that have a somewhat benefitial laws and practices against domestic violence. However, this does not seem to be a causal effect. It is highly unlikely that a countrie’s income group alone establishes the laws on domestic violence, but it points us in the direction of establishing a connection between higher levels of economic develpment and better legislation on domestic violence.
# A tibble: 4 × 2
IncomeGroup count
<fct> <int>
1 High income 39
2 Upper middle income 33
3 Lower middle income 37
4 Low income 19The problem with doing this split by regions is that the number of countries per category fluctuates a lot. There are only two high income countries compared to many Sub-Saharan African countries, which limit our possibilities of seeing patterns.
There seems to be a clear association between attitude towards violence against women and a countrie’s group income. High and upper middle income countries seem to have a lower share of women who agree that the husband is justified in inflicted violence in their wives. What are the cultural, social, and political factors that seem to change that attitude in lower middle and low income countries?
To determine whether the OECD indicator is an accurate representation of the prevalence of domestic violence against women, we decided to compare it to other available indicators on domestic violence. We have chosen the UN indicator, “Proportion of women subjected to physical and/or sexual violence by a current or former intimate partner in the last 12 months”.
There are some clear distinctions between the UN and OECD indicators. While they both measure the proportion of women who have experienced violence by an intimate partner, the UN indicator only includes women who have experienced violence in the past 12 months, while the OECD includes those who experienced violence at any point in their lives Therefore, we would expect the OECD indicator to be larger than the UN indicator for most countries, given that many more women would have experience violence at some point in their lives.
Another limitation is that the UN indicator comes from many different reference years. Some countries have collected this data in more recent years (2015), while others last collected this data in the year 2000. This discrepancy makes comparison between countries and between indicators more difficult, since peoples’ attitudes towards domestic violence and countries’ laws and regulations may have changed significantly over the years, potentially reducing the prevalence of domestic violence. In addition, all the data from the OECD indicator was collected in 2019, much more recently than most of the UN data. Nonetheless, it would be interesting to find out whether both indicators are consistent with each other.
The figure above shows two maps of the world, with colored markers to represent the prevalence of violence in different countries. The first map visualizes the OECD indicator, while the second map visualizes the UN indicator. As expected, we can see that the OECD map has much darker and larger markers in general than the UN map, suggesting that the prevalence of violence is higher in the OECD data. This makes sense since the OECD measured the prevalence of violence over womens’ lifetimes, while the UN only measured violence in the past 12 months.
The two indicators also appear to be consistent across different regions. For instance, in both maps, Europe and Northern America appear to have less prevalence of domestic violence, as shown by the smaller, lighter markers. In contrast, markers across Africa, the Middle East and South America are consistently darker and larger, revealing patterns of higher prevalence of domestic violence.
[1] 0.7561751Issues with the Data and Limitations
One tidiness issue in the data is that the dataset is in long format, which makes it difficult to visualize the relationship between the different indicators. Therefore, we had to transform the dataset into wide format to create these plots.
There are also some discrepancies between what the indicators are trying to measure and what they actually measure. Quantifying such a large-scale phenomenon as violence against women is a non-trivial effort, since violence comes in many diverse forms. For example, the prevalence in lifetime indicator only quantifies violence from an intimate partner, excluding levels of harrasment that come from outside intimate circles, such as sex trafficking. Similarly, to measure “attitude towards violence”, the dataset creators used “the percentage of women who agree that a husband/partner is justified in beating his wife/partner under certain circumstances.” Why is this particular question used as a proxy for attitude towards violence? Why not ask whether non-intimate partners are also justified in beating women? Are there better or more comprehensive questions to gauge attitude towards violence?
In addition, for the “laws on domestic violence” indicator, the dataset creators do not explain how they quantified the abstract concept of “laws and practices”. They also do not specify what they mean by laws that “fully discriminate against women’s rights”. This makes it difficult to determine the accuracy of the indicator.
The dataset also does not contain all the countries in the world.
# A tibble: 6 × 4
country alpha_code lat long
<chr> <chr> <dbl> <dbl>
1 American Samoa ASM -14.3 -170
2 Andorra AND 42.5 1.6
3 Anguilla AIA 18.2 -63.2
4 Antarctica ATA -90 0
5 Antigua and Barbuda ATG 17.0 -61.8
6 Aruba ABW 12.5 -70.0If we perform an anti-join of the countries dataset with the violence dataset, we can see that there are around 82 missing countries. Most of these countries are small islands that may not have enough data on violence against women.
Predicting Prevalence of Violence
Correlation Coefficients
Regression Model
Child Marriage Regression Model
Running /opt/R/4.1.2/lib/R/bin/R CMD SHLIB foo.c
gcc -I"/opt/R/4.1.2/lib/R/include" -DNDEBUG -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/Rcpp/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/unsupported" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/BH/include" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppParallel/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fpic -g -O2 -c foo.c -o foo.o
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:88,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name ‘namespace’
628 | namespace Eigen {
| ^~~~~~~~~
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:17: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute__’ before ‘{’ token
628 | namespace Eigen {
| ^
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:96:10: fatal error: complex: No such file or directory
96 | #include <complex>
| ^~~~~~~~~
compilation terminated.
make: *** [/opt/R/4.1.2/lib/R/etc/Makeconf:168: foo.o] Error 1 Family: gaussian
Links: mu = identity; sigma = identity
Formula: ATTITUDEVIOL ~ child_marriage
Data: prev_viol_factors (Number of observations: 163)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept 10.36 1.81 6.84 13.84 1.00 3997
child_marriage 1.20 0.12 0.97 1.43 1.00 3920
Tail_ESS
Intercept 2843
child_marriage 2465
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 17.15 0.96 15.30 19.18 1.00 3076 2428
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Labour Participation Rate Regression Model
Running /opt/R/4.1.2/lib/R/bin/R CMD SHLIB foo.c
gcc -I"/opt/R/4.1.2/lib/R/include" -DNDEBUG -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/Rcpp/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/unsupported" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/BH/include" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppParallel/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fpic -g -O2 -c foo.c -o foo.o
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:88,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name ‘namespace’
628 | namespace Eigen {
| ^~~~~~~~~
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:17: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute__’ before ‘{’ token
628 | namespace Eigen {
| ^
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:96:10: fatal error: complex: No such file or directory
96 | #include <complex>
| ^~~~~~~~~
compilation terminated.
make: *** [/opt/R/4.1.2/lib/R/etc/Makeconf:168: foo.o] Error 1 Family: gaussian
Links: mu = identity; sigma = identity
Formula: PREVVIOLLIFETIME ~ labour_part
Data: prev_viol_factors (Number of observations: 115)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept 39.59 6.31 27.55 52.05 1.00 4035
labour_part -0.30 0.12 -0.53 -0.07 1.00 4037
Tail_ESS
Intercept 3099
labour_part 3147
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 12.83 0.86 11.32 14.69 1.00 3818 2781
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Women’s Opportunities Index Regression Model
Running /opt/R/4.1.2/lib/R/bin/R CMD SHLIB foo.c
gcc -I"/opt/R/4.1.2/lib/R/include" -DNDEBUG -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/Rcpp/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/unsupported" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/BH/include" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppParallel/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fpic -g -O2 -c foo.c -o foo.o
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:88,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name ‘namespace’
628 | namespace Eigen {
| ^~~~~~~~~
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:17: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute__’ before ‘{’ token
628 | namespace Eigen {
| ^
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:96:10: fatal error: complex: No such file or directory
96 | #include <complex>
| ^~~~~~~~~
compilation terminated.
make: *** [/opt/R/4.1.2/lib/R/etc/Makeconf:168: foo.o] Error 1 Family: gaussian
Links: mu = identity; sigma = identity
Formula: prev_viol ~ womens_opp_index
Data: relevant_factors (Number of observations: 137)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS
Intercept 47.55 4.47 38.88 56.58 1.00 4103
womens_opp_index -0.35 0.07 -0.49 -0.21 1.00 4208
Tail_ESS
Intercept 2967
womens_opp_index 3018
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 13.51 0.82 12.03 15.19 1.00 4270 2891
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Women’s Education Regression Model
Running /opt/R/4.1.2/lib/R/bin/R CMD SHLIB foo.c
gcc -I"/opt/R/4.1.2/lib/R/include" -DNDEBUG -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/Rcpp/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/unsupported" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/BH/include" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/src/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppParallel/include/" -I"/cloud/lib/x86_64-pc-linux-gnu-library/4.1/rstan/include" -DEIGEN_NO_DEBUG -DBOOST_DISABLE_ASSERTS -DBOOST_PENDING_INTEGER_LOG2_HPP -DSTAN_THREADS -DBOOST_NO_AUTO_PTR -include '/cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp' -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1 -I/usr/local/include -fpic -g -O2 -c foo.c -o foo.o
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:88,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:1: error: unknown type name ‘namespace’
628 | namespace Eigen {
| ^~~~~~~~~
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/src/Core/util/Macros.h:628:17: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute__’ before ‘{’ token
628 | namespace Eigen {
| ^
In file included from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Dense:1,
from /cloud/lib/x86_64-pc-linux-gnu-library/4.1/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13,
from <command-line>:
/cloud/lib/x86_64-pc-linux-gnu-library/4.1/RcppEigen/include/Eigen/Core:96:10: fatal error: complex: No such file or directory
96 | #include <complex>
| ^~~~~~~~~
compilation terminated.
make: *** [/opt/R/4.1.2/lib/R/etc/Makeconf:168: foo.o] Error 1 Family: gaussian
Links: mu = identity; sigma = identity
Formula: prev_viol ~ education
Data: relevant_factors (Number of observations: 122)
Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
total post-warmup draws = 4000
Population-Level Effects:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
Intercept 43.72 3.88 35.88 51.38 1.00 3674 2637
education -0.27 0.05 -0.38 -0.17 1.00 3705 2703
Family Specific Parameters:
Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
sigma 12.84 0.82 11.32 14.54 1.00 4026 2957
Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
and Tail_ESS are effective sample size measures, and Rhat is the potential
scale reduction factor on split chains (at convergence, Rhat = 1).
Plot
PLOTTING ATTITUDE VIOLENCE AND LABOUR POLICY AND PRACTICE, ACCESS TO FINANCE, EDUCATION AND TRAINING AND WOMENS LEGAL STATUS
Introduction
Theory/Hypotheses
Data
Analysis and Interpretation of Results
Conclusions